AI Red Teaming
Secure Your AI Systems
AI Red Teaming involves specialized testing of AI/ML systems including model security, adversarial attacks, data poisoning, and AI-specific vulnerabilities. Our expert testers evaluate AI system security identifying vulnerabilities and testing defenses against AI-specific attacks. Testing ensures AI systems resist adversarial manipulation and protect against AI security threats.
What is AI Red Teaming?
AI Red Teaming evaluates AI/ML system security identifying vulnerabilities in AI models, training data, and AI infrastructure. Testing simulates AI-specific attacks validating security controls effectiveness. Testing covers model security, adversarial attacks, data poisoning, and AI governance ensuring comprehensive assessment.
Why AI Security Matters
AI security critical because:
- AI systems face unique security threats
- Adversarial attacks can manipulate AI models
- Data poisoning can compromise model integrity
- AI systems require specialized security testing
What We Test
Model Security
AI model vulnerabilities, security weaknesses, and robustness testing ensuring model security.
Adversarial Attacks
Adversarial examples, model evasion, and input manipulation testing model robustness.
Data Poisoning
Training data manipulation, poisoning attacks, and data integrity testing ensuring data security.
Model Extraction
Model theft, intellectual property protection, and model cloning testing IP protection.
Privacy Attacks
Membership inference, data extraction, and privacy leakage testing ensuring privacy protection.
AI Infrastructure
AI system infrastructure, deployment security, and API security testing ensuring infrastructure security.
Our Approach
1. AI System Analysis
Analyzing AI system architecture, models, deployment, and data pipelines.
2. Adversarial Testing
Testing AI models against adversarial attacks, evasion techniques, and manipulation.
3. Security Assessment
Assessing AI system security controls, vulnerabilities, and attack surfaces.
4. Reporting
Comprehensive reporting with AI security findings, risk assessment, and recommendations.
Benefits of AI Red Teaming
Vulnerability Identification
Identifies AI system vulnerabilities and attack vectors requiring attention.
Adversarial Defense
Tests defenses against adversarial attacks and manipulation ensuring model robustness.
Model Security
Improves AI model security and robustness reducing attack risk.
Compliance
Meets AI security requirements and standards including NIST AI RMF.
Risk Reduction
Reduces risk of AI-specific attacks and exploitation through vulnerability remediation.
Control Validation
Validates AI security controls and defenses ensuring proper protection.
AI Red Teaming Pricing
Our AI red teaming pricing is transparent and based on AI system complexity, model type, and testing scope.
Request a Quote
Get personalized estimate based on your AI security testing needs.
Contact Us for PricingWhat's Included:
- AI system analysis
- Adversarial testing
- Security assessment
- Comprehensive reporting
- Risk assessment
- Remediation recommendations
- Follow-up support
Note: Pricing varies based on AI system complexity, model type, testing scope, and follow-up requirements. Contact us for detailed quote.
Frequently Asked Questions (FAQ)
Find answers to common questions about AI Red Teaming:
AI Red Teaming evaluates AI/ML system security identifying vulnerabilities in AI models, training data, and AI infrastructure. Testing simulates AI-specific attacks validating security controls effectiveness. Testing covers model security, adversarial attacks, data poisoning, and AI governance ensuring comprehensive assessment.
We test various AI/ML systems including machine learning models, deep learning models, natural language processing systems, computer vision systems, recommendation systems, and other AI applications. Testing methodology adapted based on AI system type and architecture.
Testing covers model vulnerabilities, adversarial attack susceptibility, data poisoning risks, model extraction vulnerabilities, privacy leakage risks, AI infrastructure security issues, bias and fairness issues, and AI governance gaps. Findings prioritized by severity and impact.
Timeline depends on AI system complexity and testing scope. Typical timelines: Simple systems (2-3 weeks), Complex systems (3-6 weeks). Timeline includes analysis (1 week), adversarial testing (1-4 weeks), reporting (1 week). Factors: System complexity, Model type, Testing scope, Access availability.
Glocert provides comprehensive AI red teaming including AI system analysis, adversarial testing, security assessment, comprehensive reporting, risk assessment, remediation recommendations, and follow-up support. Our experienced testers have extensive experience testing various AI/ML systems following industry standards. We tailor testing approach based on your specific needs ensuring relevant findings and actionable recommendations.
Why Choose Glocert for AI Red Teaming?
AI Security Expertise
Our team includes experienced AI security testers with extensive experience testing various AI/ML systems. Testers understand AI architectures, adversarial attacks, and AI-specific vulnerabilities ensuring comprehensive testing.
Comprehensive Testing
We provide comprehensive AI security testing covering model security, adversarial attacks, data poisoning, privacy attacks, and AI infrastructure. Testing includes advanced adversarial techniques and comprehensive analysis ensuring thorough assessment.
AI Knowledge
Deep understanding of AI/ML systems, adversarial attacks, and AI security best practices. AI knowledge ensures relevant findings and actionable recommendations for AI security improvement.